A number of crisis situations, such as natural disasters, have affected the planet over the past decade. The outcomes of such disasters are catastrophic for the infrastructures of modern societies. Furthermore, after large disasters, societies come face-to-face with important issues, such as the loss of human lives, people who are missing and the increment of the criminality rate. In many occasions, they seem unprepared to face such issues. This paper aims to present an automated social media and crowdsourcing data mining system for the synchronization of the police and law enforcement agencies for the prevention of criminal activities during and post a large crisis situation.
The paper realized qualitative research in the form of a review of the literature. This review focuses on the necessity of using social media and crowdsourcing data mining techniques in combination with advanced Web technologies for the purpose of providing solutions to problems related to criminal activities caused during and after a crisis. The paper presents the ATHENA crisis management system, which uses a number of data mining techniques to collect and analyze crisis-related data from social media for the purpose of crime prevention.
Conclusions are drawn on the significance of social media and crowdsourcing data mining techniques for the resolution of problems related to large crisis situations with emphasis to the ATHENA system.
The paper shows how the integrated use of social media and data mining algorithms can contribute in the resolution of problems that are developed during and after a large crisis.
Domdouzis, K., Akhgar, B., Andrews, S., Gibson, H. and Hirsch, L. (2016), "A social media and crowdsourcing data mining system for crime prevention during and post-crisis situations", Journal of Systems and Information Technology, Vol. 18 No. 4, pp. 364-382. https://doi.org/10.1108/JSIT-06-2016-0039Download as .RIS
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